A three-dimensional finite element implementation of a simple nonassociative Drucker-Prager plasticity model with strong discontinuity mode of localized deformation is formulated for small deformations. A strong discontinuity mode of localized deformation represents slip along a surface of zero measure, whereas a weak discontinuity mode of localized deformation represents shear within a band of...

Accurate, robust and efficient multiphysics-methods for the simulation of three-dimensional complex problems with immersed boundaries from engineering as well as biophysics are a highly interesting and challenging research area. Within this context, our motivation was to develop a general framework to solve a variety of multiphysics problem classes such as fluid-structure interaction (FSI) [1]-...

We consider a variational formulation of the three-dimensional Navier–Stokes equations with mixed boundary conditions and prove that the variational problem admits a solution provided that the domain satisfies a suitable regularity assumption. Next, we propose a finite element discretization relying on the Galerkin method and establish a priori and a posteriori error estimates. Mathematics Subj...

The aim of this paper is to investigate geometrically nonlinear static analysis of axially functionally graded cantilever beam subjected to transversal non follower load. The considered problem is solved by finite element method with total Lagrangian kinematic approach. The material properties of the beam vary along the longitudinal direction according to the power law function. The finite elem...

Due to lack of theory of elasticity, estimation of ultimate torsional strength of reinforcement concrete beams is a difficult task. Therefore, the finite element methods could be applied for determination of strength of concrete beams. Furthermore, for complicated, highly nonlinear and ambiguous status, artificial neural networks are appropriate tools for prediction of behavior of such states. ...

Finite element network analysis (FENA) is a physics-informed, deep-learning-based framework for the simulation of physical systems. FENA combines conceptual flexibility classical finite methods with computational power pre-trained neural networks. A remarkable characteristic ability to simulate assemblies elements by concatenating networks serving as models classes This places in new category n...